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Denormalization is used to alter the structure of a database. Denormalization focuses on adding redundancy which means combining multiple tables so that execute queries quickly. In this article, we’ll explore Denormalization and how it impacts database design.
Denormalization is the process of adding precomputed redundant data to an otherwise normalized relational database to improve read performance. With denormalization, the database administrator selectively adds back specific instances of redundant data after the data structure has been normalized.
Database denormalization means you deliberately put the same data in several places, thus increasing redundancy. “Why denormalize a database at all?” you may ask. The main purpose of denormalization is to significantly speed up data retrieval.
In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data.
Denormalization is a strategy used on a previously-normalized database to increase performance. The idea behind it is to add redundant data where we think it will help us the most. We can use extra attributes in an existing table, add new tables, or even create instances of existing tables.
Data denormalization is the process of introducing some redundancy into previously normalized databases with the aim of optimizing database query performance. It introduces some pre-computed redundancy using different techniques to solve issues in normalized data.
Denormalization is often implemented selectively, using it only in areas where performance bottlenecks occur, ensuring the database remains efficient and manageable. Balancing Normalization and Denormalization. In practice, database design often requires a balance between normalization and denormalization.